data_vis_list.append({
'category': category_name,
'it': c_it,
'data': data_vis
})
model_counter[category_id] += 1
# Model
model = config.get_model(cfg, device=device, dataset=train_dataset)
# Intialize training
optimizer = optim.Adam(model.parameters(), lr=1e-4)
trainer = config.get_trainer(model, optimizer, cfg, device=device)
checkpoint_io = CheckpointIO(out_dir, model=model, optimizer=optimizer)
try:
load_dict = checkpoint_io.load(f'{model_name}.pt')
except FileExistsError:
load_dict = dict()
epoch_it = load_dict.get('epoch_it', -1)
it = load_dict.get('it', -1)
it0 = load_dict.get('it', -1)
metric_val_best = load_dict.get('loss_val_best',
-model_selection_sign * np.inf)
# Generator
generator = config.get_generator(model, cfg, device=device)
if metric_val_best == np.inf or metric_val_best == -np.inf:
metric_val_best = -model_selection_sign * np.inf
c_it = model_counter[category_id]
if c_it < vis_n_outputs:
data_vis_list.append({'category': category_name, 'it': c_it, 'data': data_vis})
model_counter[category_id] += 1
"""
# Model
model = config.get_model(cfg, device=device, dataset=train_dataset)
# Intialize training
optimizer = optim.Adam(model.parameters(), lr=1e-4)
trainer = config.get_trainer(model, optimizer, cfg, device=device)
checkpoint_io = CheckpointIO(out_dir, model=model, optimizer=optimizer)
try:
load_dict = checkpoint_io.load('model.pt')
except FileExistsError:
load_dict = dict()
epoch_it = load_dict.get('epoch_it', -1)
it = load_dict.get('it', -1)
it0 = load_dict.get('it', -1)
metric_val_best = load_dict.get('loss_val_best',
-model_selection_sign * np.inf)
# Generator
generator = config.get_generator(model, cfg, device=device)
if metric_val_best == np.inf or metric_val_best == -np.inf:
metric_val_best = -model_selection_sign * np.inf
out_time_file_class = os.path.join(generation_dir, 'time_generation.pkl')
batch_size = cfg['generation']['batch_size']
input_type = cfg['data']['input_type']
vis_n_outputs = cfg['generation']['vis_n_outputs']
if vis_n_outputs is None:
vis_n_outputs = -1
# Dataset
dataset = config.get_dataset('test', cfg, return_idx=True)
print(dataset)
# Model
model = config.get_model(cfg, device=device, dataset=dataset)
checkpoint_io = CheckpointIO(out_dir, model=model)
checkpoint_io.load(cfg['test']['model_file'])
# Generator
generator = config.get_generator(model, cfg, device=device)
# Determine what to generate
generate_mesh = cfg['generation']['generate_mesh']
generate_pointcloud = cfg['generation']['generate_pointcloud']
if generate_mesh and not hasattr(generator, 'generate_mesh'):
generate_mesh = False
print('Warning: generator does not support mesh generation.')
if generate_pointcloud and not hasattr(generator, 'generate_pointcloud'):
generate_pointcloud = False